Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations21102
Missing cells39941
Missing cells (%)10.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.1 MiB
Average record size in memory152.0 B

Variable types

Text2
Categorical5
Numeric11
DateTime1

Alerts

BASIN is highly overall correlated with LAT and 2 other fieldsHigh correlation
LAT is highly overall correlated with BASINHigh correlation
LON is highly overall correlated with BASINHigh correlation
NATURE is highly overall correlated with USA_SSHS and 1 other fieldsHigh correlation
SUBBASIN is highly overall correlated with BASINHigh correlation
USA_PRES is highly overall correlated with USA_SSHS and 3 other fieldsHigh correlation
USA_SSHS is highly overall correlated with NATURE and 5 other fieldsHigh correlation
USA_STATUS is highly overall correlated with NATURE and 1 other fieldsHigh correlation
USA_WIND is highly overall correlated with USA_PRES and 3 other fieldsHigh correlation
WMO_PRES is highly overall correlated with USA_PRES and 3 other fieldsHigh correlation
WMO_WIND is highly overall correlated with USA_PRES and 3 other fieldsHigh correlation
BASIN has 3619 (17.2%) missing values Missing
SUBBASIN has 2901 (13.7%) missing values Missing
WMO_WIND has 13794 (65.4%) missing values Missing
WMO_PRES has 12885 (61.1%) missing values Missing
USA_WIND has 3345 (15.9%) missing values Missing
USA_PRES has 3397 (16.1%) missing values Missing
USA_SSHS has 6884 (32.6%) zeros Zeros
STORM_DIR has 375 (1.8%) zeros Zeros
DIST2LAND has 1865 (8.8%) zeros Zeros

Reproduction

Analysis started2025-09-24 18:27:46.999951
Analysis finished2025-09-24 18:27:55.183608
Duration8.18 seconds
Software versionydata-profiling vv4.15.0
Download configurationconfig.json

Variables

SID
Text

Distinct368
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size165.0 KiB
2025-09-25T01:27:55.290409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters274326
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022008S13148
2nd row2022008S13148
3rd row2022008S13148
4th row2022008S13148
5th row2022008S13148
ValueCountFrequency (%)
2023036s12117 299
 
1.4%
2022037s10103 187
 
0.9%
2023213n14257 175
 
0.8%
2023232n13300 170
 
0.8%
2022346s10098 159
 
0.8%
2024017s15151 155
 
0.7%
2024013s10093 153
 
0.7%
2025032s14120 153
 
0.7%
2024067s10090 149
 
0.7%
2022355s10128 146
 
0.7%
Other values (358) 19356
91.7%
2025-09-25T01:27:55.467652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 76165
27.8%
0 44355
16.2%
1 37995
13.9%
3 23314
 
8.5%
4 18064
 
6.6%
5 16030
 
5.8%
N 14073
 
5.1%
6 10107
 
3.7%
7 9187
 
3.3%
9 9155
 
3.3%
Other values (2) 15881
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 274326
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 76165
27.8%
0 44355
16.2%
1 37995
13.9%
3 23314
 
8.5%
4 18064
 
6.6%
5 16030
 
5.8%
N 14073
 
5.1%
6 10107
 
3.7%
7 9187
 
3.3%
9 9155
 
3.3%
Other values (2) 15881
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 274326
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 76165
27.8%
0 44355
16.2%
1 37995
13.9%
3 23314
 
8.5%
4 18064
 
6.6%
5 16030
 
5.8%
N 14073
 
5.1%
6 10107
 
3.7%
7 9187
 
3.3%
9 9155
 
3.3%
Other values (2) 15881
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 274326
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 76165
27.8%
0 44355
16.2%
1 37995
13.9%
3 23314
 
8.5%
4 18064
 
6.6%
5 16030
 
5.8%
N 14073
 
5.1%
6 10107
 
3.7%
7 9187
 
3.3%
9 9155
 
3.3%
Other values (2) 15881
 
5.8%

SEASON
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size165.0 KiB
2023
6468 
2022
5985 
2024
5024 
2025
3574 
2026
 
51

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters84408
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2023 6468
30.7%
2022 5985
28.4%
2024 5024
23.8%
2025 3574
16.9%
2026 51
 
0.2%

Length

2025-09-25T01:27:55.529293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-25T01:27:55.586995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2023 6468
30.7%
2022 5985
28.4%
2024 5024
23.8%
2025 3574
16.9%
2026 51
 
0.2%

Most occurring characters

ValueCountFrequency (%)
2 48189
57.1%
0 21102
25.0%
3 6468
 
7.7%
4 5024
 
6.0%
5 3574
 
4.2%
6 51
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 84408
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 48189
57.1%
0 21102
25.0%
3 6468
 
7.7%
4 5024
 
6.0%
5 3574
 
4.2%
6 51
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 84408
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 48189
57.1%
0 21102
25.0%
3 6468
 
7.7%
4 5024
 
6.0%
5 3574
 
4.2%
6 51
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 84408
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 48189
57.1%
0 21102
25.0%
3 6468
 
7.7%
4 5024
 
6.0%
5 3574
 
4.2%
6 51
 
0.1%

NUMBER
Real number (ℝ)

Distinct113
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.054071
Minimum1
Maximum113
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size165.0 KiB
2025-09-25T01:27:55.657862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q120
median44
Q367
95-th percentile93
Maximum113
Range112
Interquartile range (IQR)47

Descriptive statistics

Standard deviation28.772212
Coefficient of variation (CV)0.63861514
Kurtosis-0.83177099
Mean45.054071
Median Absolute Deviation (MAD)24
Skewness0.31061984
Sum950731
Variance827.84016
MonotonicityNot monotonic
2025-09-25T01:27:55.730187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 526
 
2.5%
7 522
 
2.5%
2 453
 
2.1%
4 386
 
1.8%
44 337
 
1.6%
39 331
 
1.6%
13 312
 
1.5%
57 310
 
1.5%
36 307
 
1.5%
49 306
 
1.5%
Other values (103) 17312
82.0%
ValueCountFrequency (%)
1 204
 
1.0%
2 453
2.1%
3 143
 
0.7%
4 386
1.8%
5 166
 
0.8%
6 153
 
0.7%
7 522
2.5%
8 526
2.5%
9 190
 
0.9%
10 278
1.3%
ValueCountFrequency (%)
113 26
 
0.1%
112 146
0.7%
111 53
 
0.3%
110 159
0.8%
109 43
 
0.2%
108 53
 
0.3%
107 17
 
0.1%
106 27
 
0.1%
105 47
 
0.2%
104 49
 
0.2%
Distinct8815
Distinct (%)41.8%
Missing0
Missing (%)0.0%
Memory size165.0 KiB
Minimum2022-01-07 12:00:00
Maximum2025-09-11 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-09-25T01:27:55.805111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T01:27:55.886047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

BASIN
Categorical

High correlation  Missing 

Distinct5
Distinct (%)< 0.1%
Missing3619
Missing (%)17.2%
Memory size165.0 KiB
WP
5744 
SI
5167 
EP
3779 
SP
1862 
NI
931 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters34966
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSP
2nd rowSP
3rd rowSP
4th rowSP
5th rowSP

Common Values

ValueCountFrequency (%)
WP 5744
27.2%
SI 5167
24.5%
EP 3779
17.9%
SP 1862
 
8.8%
NI 931
 
4.4%
(Missing) 3619
17.2%

Length

2025-09-25T01:27:55.955930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-25T01:27:56.000196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
wp 5744
32.9%
si 5167
29.6%
ep 3779
21.6%
sp 1862
 
10.7%
ni 931
 
5.3%

Most occurring characters

ValueCountFrequency (%)
P 11385
32.6%
S 7029
20.1%
I 6098
17.4%
W 5744
16.4%
E 3779
 
10.8%
N 931
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34966
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 11385
32.6%
S 7029
20.1%
I 6098
17.4%
W 5744
16.4%
E 3779
 
10.8%
N 931
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34966
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 11385
32.6%
S 7029
20.1%
I 6098
17.4%
W 5744
16.4%
E 3779
 
10.8%
N 931
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34966
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 11385
32.6%
S 7029
20.1%
I 6098
17.4%
W 5744
16.4%
E 3779
 
10.8%
N 931
 
2.7%

SUBBASIN
Categorical

High correlation  Missing 

Distinct8
Distinct (%)< 0.1%
Missing2901
Missing (%)13.7%
Memory size165.0 KiB
MM
12919 
WA
2285 
EA
 
819
BB
 
632
CP
 
529
Other values (3)
 
1017

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters36402
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEA
2nd rowEA
3rd rowEA
4th rowEA
5th rowEA

Common Values

ValueCountFrequency (%)
MM 12919
61.2%
WA 2285
 
10.8%
EA 819
 
3.9%
BB 632
 
3.0%
CP 529
 
2.5%
CS 390
 
1.8%
GM 328
 
1.6%
AS 299
 
1.4%
(Missing) 2901
 
13.7%

Length

2025-09-25T01:27:56.058745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-25T01:27:56.109644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
mm 12919
71.0%
wa 2285
 
12.6%
ea 819
 
4.5%
bb 632
 
3.5%
cp 529
 
2.9%
cs 390
 
2.1%
gm 328
 
1.8%
as 299
 
1.6%

Most occurring characters

ValueCountFrequency (%)
M 26166
71.9%
A 3403
 
9.3%
W 2285
 
6.3%
B 1264
 
3.5%
C 919
 
2.5%
E 819
 
2.2%
S 689
 
1.9%
P 529
 
1.5%
G 328
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 36402
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 26166
71.9%
A 3403
 
9.3%
W 2285
 
6.3%
B 1264
 
3.5%
C 919
 
2.5%
E 819
 
2.2%
S 689
 
1.9%
P 529
 
1.5%
G 328
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 36402
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 26166
71.9%
A 3403
 
9.3%
W 2285
 
6.3%
B 1264
 
3.5%
C 919
 
2.5%
E 819
 
2.2%
S 689
 
1.9%
P 529
 
1.5%
G 328
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 36402
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 26166
71.9%
A 3403
 
9.3%
W 2285
 
6.3%
B 1264
 
3.5%
C 919
 
2.5%
E 819
 
2.2%
S 689
 
1.9%
P 529
 
1.5%
G 328
 
0.9%

NAME
Text

Distinct297
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size165.0 KiB
2025-09-25T01:27:56.279668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length7
Mean length5.7813951
Min length3

Characters and Unicode

Total characters121999
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTIFFANY
2nd rowTIFFANY
3rd rowTIFFANY
4th rowTIFFANY
5th rowTIFFANY
ValueCountFrequency (%)
unnamed 1861
 
8.8%
freddy 299
 
1.4%
emnati 187
 
0.9%
dora 175
 
0.8%
franklin 170
 
0.8%
darian 159
 
0.8%
kirrily 155
 
0.7%
taliah 153
 
0.7%
anggrek 153
 
0.7%
neville 149
 
0.7%
Other values (287) 17641
83.6%
2025-09-25T01:27:56.511636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 17577
14.4%
N 13932
11.4%
E 12594
10.3%
I 11312
 
9.3%
L 7662
 
6.3%
R 7286
 
6.0%
O 6143
 
5.0%
D 5569
 
4.6%
M 5484
 
4.5%
U 3953
 
3.2%
Other values (16) 30487
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 121999
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 17577
14.4%
N 13932
11.4%
E 12594
10.3%
I 11312
 
9.3%
L 7662
 
6.3%
R 7286
 
6.0%
O 6143
 
5.0%
D 5569
 
4.6%
M 5484
 
4.5%
U 3953
 
3.2%
Other values (16) 30487
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 121999
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 17577
14.4%
N 13932
11.4%
E 12594
10.3%
I 11312
 
9.3%
L 7662
 
6.3%
R 7286
 
6.0%
O 6143
 
5.0%
D 5569
 
4.6%
M 5484
 
4.5%
U 3953
 
3.2%
Other values (16) 30487
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 121999
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 17577
14.4%
N 13932
11.4%
E 12594
10.3%
I 11312
 
9.3%
L 7662
 
6.3%
R 7286
 
6.0%
O 6143
 
5.0%
D 5569
 
4.6%
M 5484
 
4.5%
U 3953
 
3.2%
Other values (16) 30487
25.0%

LAT
Real number (ℝ)

High correlation 

Distinct994
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.7795375
Minimum-50.3
Maximum70.1
Zeros0
Zeros (%)0.0%
Negative7029
Negative (%)33.3%
Memory size165.0 KiB
2025-09-25T01:27:56.582115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-50.3
5-th percentile-24.4
Q1-13.7
median15.2
Q322.3
95-th percentile38.9
Maximum70.1
Range120.4
Interquartile range (IQR)36

Descriptive statistics

Standard deviation21.018416
Coefficient of variation (CV)2.3940231
Kurtosis-0.85677698
Mean8.7795375
Median Absolute Deviation (MAD)12
Skewness-0.27771667
Sum185265.8
Variance441.77379
MonotonicityNot monotonic
2025-09-25T01:27:56.653660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 108
 
0.5%
15.8 105
 
0.5%
16.6 100
 
0.5%
17.4 98
 
0.5%
17.1 95
 
0.5%
15.7 95
 
0.5%
17.2 91
 
0.4%
17 90
 
0.4%
17.3 89
 
0.4%
14.6 89
 
0.4%
Other values (984) 20142
95.5%
ValueCountFrequency (%)
-50.3 1
< 0.1%
-49.5 1
< 0.1%
-48.7 1
< 0.1%
-47.9 1
< 0.1%
-47.1 1
< 0.1%
-46.3 1
< 0.1%
-45.6 1
< 0.1%
-45.3 1
< 0.1%
-45 1
< 0.1%
-44.4 1
< 0.1%
ValueCountFrequency (%)
70.1 1
< 0.1%
69.9 1
< 0.1%
69.8 1
< 0.1%
69.7 1
< 0.1%
69.5 1
< 0.1%
69.3 1
< 0.1%
69 1
< 0.1%
68.7 1
< 0.1%
68.5 1
< 0.1%
68.3 1
< 0.1%

LON
Real number (ℝ)

High correlation 

Distinct3223
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.333504
Minimum-179.8
Maximum257.4
Zeros0
Zeros (%)0.0%
Negative7277
Negative (%)34.5%
Memory size165.0 KiB
2025-09-25T01:27:56.724844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-179.8
5-th percentile-125.4
Q1-60.1
median86.8
Q3130.3
95-th percentile168.2
Maximum257.4
Range437.2
Interquartile range (IQR)190.4

Descriptive statistics

Standard deviation104.23555
Coefficient of variation (CV)2.299305
Kurtosis-1.2376812
Mean45.333504
Median Absolute Deviation (MAD)56.6
Skewness-0.51341948
Sum956627.6
Variance10865.05
MonotonicityNot monotonic
2025-09-25T01:27:56.905321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
124.7 32
 
0.2%
127.4 31
 
0.1%
126 30
 
0.1%
125 29
 
0.1%
125.6 29
 
0.1%
130.3 29
 
0.1%
113.6 28
 
0.1%
123.4 27
 
0.1%
124.2 27
 
0.1%
94.1 27
 
0.1%
Other values (3213) 20813
98.6%
ValueCountFrequency (%)
-179.8 1
 
< 0.1%
-179.4 2
< 0.1%
-179.3 1
 
< 0.1%
-179.2 2
< 0.1%
-179.1 1
 
< 0.1%
-179 4
< 0.1%
-178.8 1
 
< 0.1%
-178.7 2
< 0.1%
-178.5 2
< 0.1%
-178.4 1
 
< 0.1%
ValueCountFrequency (%)
257.4 1
< 0.1%
256.9 1
< 0.1%
256.3 1
< 0.1%
255.7 1
< 0.1%
255 1
< 0.1%
254.3 1
< 0.1%
253.6 1
< 0.1%
252.9 1
< 0.1%
252.2 1
< 0.1%
251.4 1
< 0.1%

WMO_WIND
Real number (ℝ)

High correlation  Missing 

Distinct49
Distinct (%)0.7%
Missing13794
Missing (%)65.4%
Infinite0
Infinite (%)0.0%
Mean47.496032
Minimum10
Maximum155
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size165.0 KiB
2025-09-25T01:27:56.974314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile20
Q130
median40
Q360
95-th percentile100
Maximum155
Range145
Interquartile range (IQR)30

Descriptive statistics

Standard deviation24.530581
Coefficient of variation (CV)0.51647643
Kurtosis0.93501705
Mean47.496032
Median Absolute Deviation (MAD)15
Skewness1.175352
Sum347101
Variance601.7494
MonotonicityNot monotonic
2025-09-25T01:27:57.045236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
35 918
 
4.4%
30 913
 
4.3%
25 892
 
4.2%
40 614
 
2.9%
45 572
 
2.7%
50 497
 
2.4%
20 429
 
2.0%
55 339
 
1.6%
75 243
 
1.2%
60 240
 
1.1%
Other values (39) 1651
 
7.8%
(Missing) 13794
65.4%
ValueCountFrequency (%)
10 5
 
< 0.1%
15 140
 
0.7%
20 429
2.0%
22 5
 
< 0.1%
23 3
 
< 0.1%
25 892
4.2%
27 7
 
< 0.1%
28 7
 
< 0.1%
29 1
 
< 0.1%
30 913
4.3%
ValueCountFrequency (%)
155 1
 
< 0.1%
150 1
 
< 0.1%
145 7
 
< 0.1%
140 12
 
0.1%
135 9
 
< 0.1%
130 11
 
0.1%
125 18
 
0.1%
120 21
 
0.1%
115 69
0.3%
110 46
0.2%

WMO_PRES
Real number (ℝ)

High correlation  Missing 

Distinct111
Distinct (%)1.4%
Missing12885
Missing (%)61.1%
Infinite0
Infinite (%)0.0%
Mean990.46903
Minimum895
Maximum1016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size165.0 KiB
2025-09-25T01:27:57.118369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum895
5-th percentile948
Q1984
median998
Q31004
95-th percentile1009
Maximum1016
Range121
Interquartile range (IQR)20

Descriptive statistics

Standard deviation19.41084
Coefficient of variation (CV)0.019597625
Kurtosis2.5690864
Mean990.46903
Median Absolute Deviation (MAD)8
Skewness-1.6453224
Sum8138684
Variance376.78072
MonotonicityNot monotonic
2025-09-25T01:27:57.193626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1004 531
 
2.5%
1000 486
 
2.3%
998 463
 
2.2%
1002 446
 
2.1%
1006 433
 
2.1%
996 293
 
1.4%
1007 275
 
1.3%
1008 266
 
1.3%
1005 266
 
1.3%
990 223
 
1.1%
Other values (101) 4535
 
21.5%
(Missing) 12885
61.1%
ValueCountFrequency (%)
895 1
 
< 0.1%
900 6
< 0.1%
902 2
 
< 0.1%
905 6
< 0.1%
907 1
 
< 0.1%
908 2
 
< 0.1%
909 1
 
< 0.1%
910 9
< 0.1%
913 1
 
< 0.1%
914 1
 
< 0.1%
ValueCountFrequency (%)
1016 2
 
< 0.1%
1015 5
 
< 0.1%
1014 15
 
0.1%
1013 15
 
0.1%
1012 79
 
0.4%
1011 62
 
0.3%
1010 126
0.6%
1009 142
0.7%
1008 266
1.3%
1007 275
1.3%

USA_WIND
Real number (ℝ)

High correlation  Missing 

Distinct133
Distinct (%)0.7%
Missing3345
Missing (%)15.9%
Infinite0
Infinite (%)0.0%
Mean48.855663
Minimum10
Maximum165
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size165.0 KiB
2025-09-25T01:27:57.264405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile20
Q130
median40
Q360
95-th percentile109
Maximum165
Range155
Interquartile range (IQR)30

Descriptive statistics

Standard deviation26.853953
Coefficient of variation (CV)0.54965896
Kurtosis1.2118269
Mean48.855663
Median Absolute Deviation (MAD)14
Skewness1.3131745
Sum867530
Variance721.13477
MonotonicityNot monotonic
2025-09-25T01:27:57.337491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 1869
 
8.9%
35 1667
 
7.9%
30 1529
 
7.2%
45 1273
 
6.0%
40 843
 
4.0%
20 834
 
4.0%
50 568
 
2.7%
55 492
 
2.3%
60 434
 
2.1%
29 368
 
1.7%
Other values (123) 7880
37.3%
(Missing) 3345
15.9%
ValueCountFrequency (%)
10 4
 
< 0.1%
13 4
 
< 0.1%
15 313
 
1.5%
16 2
 
< 0.1%
17 1
 
< 0.1%
18 70
 
0.3%
19 33
 
0.2%
20 834
4.0%
21 1
 
< 0.1%
22 18
 
0.1%
ValueCountFrequency (%)
165 2
 
< 0.1%
163 1
 
< 0.1%
160 4
 
< 0.1%
155 8
 
< 0.1%
153 2
 
< 0.1%
150 4
 
< 0.1%
145 15
0.1%
144 1
 
< 0.1%
143 5
 
< 0.1%
140 28
0.1%

USA_PRES
Real number (ℝ)

High correlation  Missing 

Distinct122
Distinct (%)0.7%
Missing3397
Missing (%)16.1%
Infinite0
Infinite (%)0.0%
Mean990.60921
Minimum891
Maximum1017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size165.0 KiB
2025-09-25T01:27:57.411081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum891
5-th percentile949
Q1984
median997
Q31004
95-th percentile1009
Maximum1017
Range126
Interquartile range (IQR)20

Descriptive statistics

Standard deviation18.780102
Coefficient of variation (CV)0.018958134
Kurtosis2.4149982
Mean990.60921
Median Absolute Deviation (MAD)8
Skewness-1.5828655
Sum17538736
Variance352.69222
MonotonicityNot monotonic
2025-09-25T01:27:57.483296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1007 835
 
4.0%
1006 758
 
3.6%
1000 756
 
3.6%
1004 693
 
3.3%
998 679
 
3.2%
999 646
 
3.1%
1002 631
 
3.0%
1005 621
 
2.9%
1003 607
 
2.9%
1001 576
 
2.7%
Other values (112) 10903
51.7%
(Missing) 3397
 
16.1%
ValueCountFrequency (%)
891 1
 
< 0.1%
894 1
 
< 0.1%
895 1
 
< 0.1%
897 4
< 0.1%
899 1
 
< 0.1%
900 1
 
< 0.1%
901 1
 
< 0.1%
902 4
< 0.1%
903 2
< 0.1%
904 2
< 0.1%
ValueCountFrequency (%)
1017 10
 
< 0.1%
1016 3
 
< 0.1%
1015 18
 
0.1%
1014 36
 
0.2%
1013 31
 
0.1%
1012 118
 
0.6%
1011 148
 
0.7%
1010 350
1.7%
1009 449
2.1%
1008 535
2.5%

USA_SSHS
Real number (ℝ)

High correlation  Zeros 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.068003
Minimum-5
Maximum5
Zeros6884
Zeros (%)32.6%
Negative10233
Negative (%)48.5%
Memory size165.0 KiB
2025-09-25T01:27:57.540397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-5
5-th percentile-5
Q1-3
median0
Q30
95-th percentile3
Maximum5
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4020296
Coefficient of variation (CV)-2.249085
Kurtosis-0.53277273
Mean-1.068003
Median Absolute Deviation (MAD)1
Skewness-0.1129456
Sum-22537
Variance5.7697463
MonotonicityNot monotonic
2025-09-25T01:27:57.588496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 6884
32.6%
-5 3280
15.5%
-1 3179
15.1%
-3 2883
13.7%
1 1662
 
7.9%
2 863
 
4.1%
3 704
 
3.3%
4 671
 
3.2%
-4 568
 
2.7%
-2 323
 
1.5%
ValueCountFrequency (%)
-5 3280
15.5%
-4 568
 
2.7%
-3 2883
13.7%
-2 323
 
1.5%
-1 3179
15.1%
0 6884
32.6%
1 1662
 
7.9%
2 863
 
4.1%
3 704
 
3.3%
4 671
 
3.2%
ValueCountFrequency (%)
5 85
 
0.4%
4 671
 
3.2%
3 704
 
3.3%
2 863
 
4.1%
1 1662
 
7.9%
0 6884
32.6%
-1 3179
15.1%
-2 323
 
1.5%
-3 2883
13.7%
-4 568
 
2.7%

STORM_SPEED
Real number (ℝ)

Distinct57
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.843285
Minimum0
Maximum58
Zeros41
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size165.0 KiB
2025-09-25T01:27:57.652030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q16
median9
Q313
95-th percentile21
Maximum58
Range58
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.3255471
Coefficient of variation (CV)0.64262562
Kurtosis5.8514985
Mean9.843285
Median Absolute Deviation (MAD)3
Skewness1.8383807
Sum207713
Variance40.012546
MonotonicityNot monotonic
2025-09-25T01:27:57.727918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 1800
 
8.5%
7 1726
 
8.2%
6 1703
 
8.1%
9 1568
 
7.4%
5 1445
 
6.8%
4 1445
 
6.8%
10 1425
 
6.8%
11 1266
 
6.0%
12 1167
 
5.5%
3 1106
 
5.2%
Other values (47) 6451
30.6%
ValueCountFrequency (%)
0 41
 
0.2%
1 355
 
1.7%
2 713
 
3.4%
3 1106
5.2%
4 1445
6.8%
5 1445
6.8%
6 1703
8.1%
7 1726
8.2%
8 1800
8.5%
9 1568
7.4%
ValueCountFrequency (%)
58 1
 
< 0.1%
57 2
 
< 0.1%
54 1
 
< 0.1%
53 1
 
< 0.1%
52 2
 
< 0.1%
51 1
 
< 0.1%
50 2
 
< 0.1%
49 7
< 0.1%
48 3
< 0.1%
47 3
< 0.1%

STORM_DIR
Real number (ℝ)

Zeros 

Distinct73
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean211.0127
Minimum0
Maximum360
Zeros375
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size165.0 KiB
2025-09-25T01:27:57.802255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q1115
median255
Q3290
95-th percentile335
Maximum360
Range360
Interquartile range (IQR)175

Descriptive statistics

Standard deviation105.41616
Coefficient of variation (CV)0.49957257
Kurtosis-0.98549012
Mean211.0127
Median Absolute Deviation (MAD)55
Skewness-0.64496129
Sum4452790
Variance11112.566
MonotonicityNot monotonic
2025-09-25T01:27:57.877836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
270 969
 
4.6%
280 860
 
4.1%
275 788
 
3.7%
285 782
 
3.7%
290 723
 
3.4%
295 695
 
3.3%
265 662
 
3.1%
300 632
 
3.0%
260 526
 
2.5%
305 509
 
2.4%
Other values (63) 13956
66.1%
ValueCountFrequency (%)
0 375
1.8%
5 202
1.0%
10 247
1.2%
15 260
1.2%
20 190
0.9%
25 251
1.2%
30 268
1.3%
35 234
1.1%
40 229
1.1%
45 262
1.2%
ValueCountFrequency (%)
360 57
 
0.3%
355 220
1.0%
350 226
1.1%
345 269
1.3%
340 253
1.2%
335 301
1.4%
330 321
1.5%
325 338
1.6%
320 353
1.7%
315 494
2.3%

NATURE
Categorical

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size165.0 KiB
TS
13721 
DS
2669 
NR
2527 
ET
 
1112
MX
 
761

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters42204
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMX
2nd rowMX
3rd rowMX
4th rowMX
5th rowTS

Common Values

ValueCountFrequency (%)
TS 13721
65.0%
DS 2669
 
12.6%
NR 2527
 
12.0%
ET 1112
 
5.3%
MX 761
 
3.6%
SS 312
 
1.5%

Length

2025-09-25T01:27:57.944243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-25T01:27:57.986377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ts 13721
65.0%
ds 2669
 
12.6%
nr 2527
 
12.0%
et 1112
 
5.3%
mx 761
 
3.6%
ss 312
 
1.5%

Most occurring characters

ValueCountFrequency (%)
S 17014
40.3%
T 14833
35.1%
D 2669
 
6.3%
N 2527
 
6.0%
R 2527
 
6.0%
E 1112
 
2.6%
M 761
 
1.8%
X 761
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 42204
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 17014
40.3%
T 14833
35.1%
D 2669
 
6.3%
N 2527
 
6.0%
R 2527
 
6.0%
E 1112
 
2.6%
M 761
 
1.8%
X 761
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 42204
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 17014
40.3%
T 14833
35.1%
D 2669
 
6.3%
N 2527
 
6.0%
R 2527
 
6.0%
E 1112
 
2.6%
M 761
 
1.8%
X 761
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 42204
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 17014
40.3%
T 14833
35.1%
D 2669
 
6.3%
N 2527
 
6.0%
R 2527
 
6.0%
E 1112
 
2.6%
M 761
 
1.8%
X 761
 
1.8%

USA_STATUS
Categorical

High correlation 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size165.0 KiB
7524 
TS
4586 
TD
2246 
HU
1504 
DB
1477 
Other values (8)
3765 

Length

Max length2
Median length2
Mean length1.6434461
Min length1

Characters and Unicode

Total characters34680
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDB
2nd rowDB
3rd rowDB
4th rowDB
5th rowTD

Common Values

ValueCountFrequency (%)
7524
35.7%
TS 4586
21.7%
TD 2246
 
10.6%
HU 1504
 
7.1%
DB 1477
 
7.0%
LO 1380
 
6.5%
TY 1334
 
6.3%
EX 568
 
2.7%
SS 219
 
1.0%
ST 134
 
0.6%
Other values (3) 130
 
0.6%

Length

2025-09-25T01:27:58.047050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ts 4586
33.8%
td 2246
16.5%
hu 1504
 
11.1%
db 1477
 
10.9%
lo 1380
 
10.2%
ty 1334
 
9.8%
ex 568
 
4.2%
ss 219
 
1.6%
st 134
 
1.0%
sd 104
 
0.8%
Other values (2) 26
 
0.2%

Most occurring characters

ValueCountFrequency (%)
T 8300
23.9%
7524
21.7%
S 5262
15.2%
D 3849
11.1%
H 1504
 
4.3%
U 1504
 
4.3%
B 1477
 
4.3%
L 1380
 
4.0%
O 1380
 
4.0%
Y 1334
 
3.8%
Other values (5) 1166
 
3.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34680
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 8300
23.9%
7524
21.7%
S 5262
15.2%
D 3849
11.1%
H 1504
 
4.3%
U 1504
 
4.3%
B 1477
 
4.3%
L 1380
 
4.0%
O 1380
 
4.0%
Y 1334
 
3.8%
Other values (5) 1166
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34680
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 8300
23.9%
7524
21.7%
S 5262
15.2%
D 3849
11.1%
H 1504
 
4.3%
U 1504
 
4.3%
B 1477
 
4.3%
L 1380
 
4.0%
O 1380
 
4.0%
Y 1334
 
3.8%
Other values (5) 1166
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34680
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 8300
23.9%
7524
21.7%
S 5262
15.2%
D 3849
11.1%
H 1504
 
4.3%
U 1504
 
4.3%
B 1477
 
4.3%
L 1380
 
4.0%
O 1380
 
4.0%
Y 1334
 
3.8%
Other values (5) 1166
 
3.4%

DIST2LAND
Real number (ℝ)

Zeros 

Distinct2577
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean763.49441
Minimum0
Maximum3219
Zeros1865
Zeros (%)8.8%
Negative0
Negative (%)0.0%
Memory size165.0 KiB
2025-09-25T01:27:58.108380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1197
median577
Q31231
95-th percentile2051
Maximum3219
Range3219
Interquartile range (IQR)1034

Descriptive statistics

Standard deviation673.07942
Coefficient of variation (CV)0.88157741
Kurtosis-0.25462019
Mean763.49441
Median Absolute Deviation (MAD)461
Skewness0.80335973
Sum16111259
Variance453035.91
MonotonicityNot monotonic
2025-09-25T01:27:58.294298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1865
 
8.8%
10 89
 
0.4%
15 58
 
0.3%
11 54
 
0.3%
44 53
 
0.3%
24 53
 
0.3%
22 44
 
0.2%
30 42
 
0.2%
78 40
 
0.2%
89 39
 
0.2%
Other values (2567) 18765
88.9%
ValueCountFrequency (%)
0 1865
8.8%
5 1
 
< 0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
8 5
 
< 0.1%
9 22
 
0.1%
10 89
 
0.4%
11 54
 
0.3%
13 3
 
< 0.1%
14 20
 
0.1%
ValueCountFrequency (%)
3219 1
< 0.1%
3209 1
< 0.1%
3163 1
< 0.1%
3159 1
< 0.1%
3154 2
< 0.1%
3151 1
< 0.1%
3146 1
< 0.1%
3144 1
< 0.1%
3143 1
< 0.1%
3140 1
< 0.1%

Interactions

2025-09-25T01:27:54.108591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T01:27:47.798277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T01:27:48.405391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T01:27:49.080196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T01:27:49.649627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-09-25T01:27:51.601227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-09-25T01:27:47.967929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T01:27:48.557956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-09-25T01:27:51.143215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-09-25T01:27:48.024572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T01:27:48.614190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-09-25T01:27:49.874636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T01:27:50.497901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T01:27:51.201919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T01:27:51.816381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T01:27:52.416274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-09-25T01:27:48.351604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T01:27:48.943485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T01:27:49.597875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T01:27:50.219747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T01:27:50.830797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T01:27:51.542739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T01:27:52.152267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T01:27:52.733251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T01:27:53.434887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T01:27:54.050235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-09-25T01:27:58.358270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
BASINDIST2LANDLATLONNATURENUMBERSEASONSTORM_DIRSTORM_SPEEDSUBBASINUSA_PRESUSA_SSHSUSA_STATUSUSA_WINDWMO_PRESWMO_WIND
BASIN1.0000.1790.5480.6340.2620.4330.1380.3460.1040.6800.2230.2120.3770.0760.1920.215
DIST2LAND0.1791.000-0.042-0.0960.0850.0240.1070.0330.1070.1390.1270.0470.088-0.0050.1690.027
LAT0.548-0.0421.000-0.1080.3780.4980.1310.0270.1570.282-0.010-0.0050.2530.042-0.0730.273
LON0.634-0.096-0.1081.0000.271-0.1290.122-0.172-0.0100.489-0.266-0.1420.2830.001-0.2250.059
NATURE0.2620.0850.3780.2711.0000.1460.2560.1830.1580.1370.2820.5720.6610.2850.2230.227
NUMBER0.4330.0240.498-0.1290.1461.0000.1630.1470.0210.2230.1170.0220.167-0.0660.0910.090
SEASON0.1380.1070.1310.1220.2560.1631.0000.0630.0410.1430.0790.1340.3100.1070.0760.081
STORM_DIR0.3460.0330.027-0.1720.1830.1470.0631.000-0.0220.1480.0490.1460.1280.0370.0210.092
STORM_SPEED0.1040.1070.157-0.0100.1580.0210.041-0.0221.0000.104-0.0830.0440.0940.136-0.1100.198
SUBBASIN0.6800.1390.2820.4890.1370.2230.1430.1480.1041.0000.0960.1090.1710.0630.1260.156
USA_PRES0.2230.127-0.010-0.2660.2820.1170.0790.049-0.0830.0961.000-0.7530.392-0.8480.964-0.869
USA_SSHS0.2120.047-0.005-0.1420.5720.0220.1340.1460.0440.109-0.7531.0000.6650.870-0.6510.850
USA_STATUS0.3770.0880.2530.2830.6610.1670.3100.1280.0940.1710.3920.6651.0000.4220.3470.374
USA_WIND0.076-0.0050.0420.0010.285-0.0660.1070.0370.1360.063-0.8480.8700.4221.000-0.8690.967
WMO_PRES0.1920.169-0.073-0.2250.2230.0910.0760.021-0.1100.1260.964-0.6510.347-0.8691.000-0.848
WMO_WIND0.2150.0270.2730.0590.2270.0900.0810.0920.1980.156-0.8690.8500.3740.967-0.8481.000

Missing values

2025-09-25T01:27:54.767543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-25T01:27:54.994943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-09-25T01:27:55.127917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

SIDSEASONNUMBERISO_TIMEBASINSUBBASINNAMELATLONWMO_WINDWMO_PRESUSA_WINDUSA_PRESUSA_SSHSSTORM_SPEEDSTORM_DIRNATUREUSA_STATUSDIST2LAND
02022008S13148202212022-01-08 00:00:00SPEATIFFANY-12.6147.725.01005.020.01007.0-36160MXDB270
12022008S13148202212022-01-08 03:00:00SPEATIFFANY-12.9147.8NaNNaN20.01007.0-35160MXDB303
22022008S13148202212022-01-08 06:00:00SPEATIFFANY-13.1147.925.01004.020.01007.0-34160MXDB324
32022008S13148202212022-01-08 09:00:00SPEATIFFANY-13.2147.9NaNNaN23.01006.0-32165MXDB336
42022008S13148202212022-01-08 12:00:00SPEATIFFANY-13.3148.030.01004.025.01004.0-130TSTD347
52022008S13148202212022-01-08 15:00:00SPEATIFFANY-13.5148.0NaNNaN28.01003.0-14195TSTD336
62022008S13148202212022-01-08 18:00:00SPEATIFFANY-13.7147.930.01002.030.01001.0-15230TSTD315
72022008S13148202212022-01-08 21:00:00SPEATIFFANY-13.8147.5NaNNaN33.01000.0-17250TSTD272
82022008S13148202212022-01-09 00:00:00SPEATIFFANY-14.0147.230.01000.035.0998.005250TSTS238
92022008S13148202212022-01-09 03:00:00SPEATIFFANY-14.0147.0NaNNaN40.0997.004255TSTS214
SIDSEASONNUMBERISO_TIMEBASINSUBBASINNAMELATLONWMO_WINDWMO_PRESUSA_WINDUSA_PRESUSA_SSHSSTORM_SPEEDSTORM_DIRNATUREUSA_STATUSDIST2LAND
210922025252S090692026722025-09-09 21:00:00SIMMUNNAMED-10.466.6NaNNaN35.01006.0011260NR1445
210932025252S090692026722025-09-10 00:00:00SIMMUNNAMED-10.566.1NaNNaN35.01006.009265NR1401
210942025252S090692026722025-09-10 03:00:00SIMMUNNAMED-10.565.7NaNNaN35.01005.007270NR1374
210952025252S090692026722025-09-10 06:00:00SIMMUNNAMED-10.565.4NaNNaN35.01004.005270NR1354
210962025252S090692026722025-09-10 09:00:00SIMMUNNAMED-10.565.2NaNNaN35.01004.004270NR1341
210972025252S090692026722025-09-10 12:00:00SIMMUNNAMED-10.565.0NaNNaN35.01004.004295NR1329
210982025252S090692026722025-09-10 15:00:00SIMMUNNAMED-10.464.8NaNNaN35.01005.004315NR1325
210992025252S090692026722025-09-10 18:00:00SIMMUNNAMED-10.264.7NaNNaN35.01006.003320NR1338
211002025252S090692026722025-09-10 21:00:00SIMMUNNAMED-10.164.6NaNNaN35.01006.001315NR1341
211012025252S090692026722025-09-11 00:00:00SIMMUNNAMED-10.164.6NaNNaN35.01006.001305NR1341